Ian Clark runs Crescendo Consulting, helping companies monetize AI features. He's advised leadership teams on packaging, pricing metrics, and outcome-based models. And of course, he performed his obligatory stint at Simon Kutcher Partners.

In this episode, Ian challenges common misconceptions about AI pricing, explaining why cost-plus pricing is still wrong even with variable AI costs, how to choose the right pricing metrics beyond tokens, and why outcome-based pricing isn't the silver bullet many believe it to be.

 

Why you have to check out today's podcast: 

• Understand why AI costs shouldn't drive your pricing strategy, even when margins drop below traditional SaaS levels. 
• Learn how to identify pricing metrics that correlate with willingness to pay rather than falling into the token-based pricing trap.
• Discover why outcome-based pricing for AI faces fundamental attribution problems that make it less viable than expected.

 

"The best way to get willingness-to-pay data and to understand where the value of your product comes from is by doing customer interviews, not the testing, not the data, customer interviews." 

– Ian Clark

 

Topics Covered:

02:59 - Monetizing AI vs. SaaS. The surprising similarities between AI and SaaS pricing, and why cost-plus pricing remains a bad idea even with AI's variable costs.

05:17 - Pricing Strategy in AI.The gross margin threshold where revenue-optimizing and margin-optimizing prices diverge (50-60%), plus the potato chip pricing thought experiment.

09:35 - AI Pricing Strategies.Why token-based pricing is problematic and how to find the right pricing metric that correlates with willingness to pay.

12:24 - Pricing Strategies for AI Tools.Real-world case study of sales enablement AI: choosing between user-based vs. usage-based pricing based on wallet size indicators.

16:27 - Outcomes-based Pricing Skepticism.The attribution problem with outcomes-based pricing and why it's harder to implement than it appears, using grocery shrinkage AI as an example.

20:11 - Outcome-based Pricing for AI. Sierra's "resolved conversations" model critique and the ethics of incentivizing AI agents vs. human labor.

24:03 - Pricing and Value Creation.The three-layer value framework: actual economic value → perceived value → willingness to pay, accounting for risk, timing, and budget constraints.

26:39 - 10% Rule in Pricing Strategy.Debunking the "charge 10% of value created" rule with Y Combinator math showing how small variations (9X vs 11X) can dramatically impact company survival.

30:31 - Customer Interviews for Pricing Insights.Why customer interviews beat data analysis and A/B testing for understanding willingness to pay and pain points.

 

Key Takeaways:

"We have known for a very long time that cost-plus pricing is a really bad idea. And it's not the case that now suddenly that we have AI, now suddenly it's a good idea." - Ian Clark

"So the 10x rule, it's great, but it's just woefully insufficient. Why not 11x? Why not 9x? You actually don't know." - Ian Clark

"One thing that we like to say about pricing and monetization is that people think it's like architecture, but it really should be like gardening." - Ian Clark

 

People / Resources Mentioned:

• Simon Kutcher Partners: https://www.simon-kucher.com/en
• Alpine Investors: https://alpineinvestors.com/
• James Wilton: https://www.linkedin.com/in/jamesdwilton/
• Sierra: https://sierra.ai/
• Fin AI: https://fin.ai/
• Y Combinator: https://www.ycombinator.com/
• McKinsey: https://www.mckinsey.com/

 

Connect with Ian Clark:

• Website: https://crescendo.consulting
• LinkedIn: https://www.linkedin.com/in/ian-harrison-clark/
• Email: [email protected]

 

Connect with Mark Stiving:

• LinkedIn: https://www.linkedin.com/in/stiving/
• Email: [email protected]